Sungpill Choi

908 total citations
36 papers, 682 citations indexed

About

Sungpill Choi is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Computer Networks and Communications. According to data from OpenAlex, Sungpill Choi has authored 36 papers receiving a total of 682 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 17 papers in Electrical and Electronic Engineering and 7 papers in Computer Networks and Communications. Recurrent topics in Sungpill Choi's work include CCD and CMOS Imaging Sensors (10 papers), Advanced Memory and Neural Computing (9 papers) and Advanced Neural Network Applications (6 papers). Sungpill Choi is often cited by papers focused on CCD and CMOS Imaging Sensors (10 papers), Advanced Memory and Neural Computing (9 papers) and Advanced Neural Network Applications (6 papers). Sungpill Choi collaborates with scholars based in South Korea. Sungpill Choi's co-authors include Hoi‐Jun Yoo, Kyeongryeol Bong, Changhyeon Kim, Sanghoon Kang, Donghyeon Han, Dongjoo Shin, Jinmook Lee, Seong‐Wook Park, Kyuho Lee and Youchang Kim and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Applied Surface Science and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Sungpill Choi

34 papers receiving 659 citations

Peers

Sungpill Choi

EEE

CVPR

CNC

Kyeongryeol Bong South Korea

Juhyoung Lee South Korea

Jong Hwan Ko South Korea

Sanghoon Kang South Korea

Lingzhi Sui China

Shimpei Sato Japan

Liangzhen Lai United States

Bert Moons Belgium

Roberto Muscedere Canada

Berin Martini United States

Kyeongryeol Bong South Korea

Sungpill Choi

410 ×0.9

EEE

325 ×1.0

CVPR

101 ×0.7

59 ×0.9

CNC

59 ×0.9

Citations per year, relative to Sungpill Choi Sungpill Choi (= 1×) peers Kyeongryeol Bong

Countries citing papers authored by Sungpill Choi

Since Specialization

Citations

This map shows the geographic impact of Sungpill Choi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sungpill Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungpill Choi more than expected).

Fields of papers citing papers by Sungpill Choi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sungpill Choi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sungpill Choi. The network helps show where Sungpill Choi may publish in the future.

Co-authorship network of co-authors of Sungpill Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Sungpill Choi. A scholar is included among the top collaborators of Sungpill Choi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sungpill Choi. Sungpill Choi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Im, Dongseok, Sanghoon Kang, Donghyeon Han, Sungpill Choi, & Hoi‐Jun Yoo. (2020). A 4.45 ms Low-Latency 3D Point-Cloud-Based Neural Network Processor for Hand Pose Estimation in Immersive Wearable Devices. 1–2. 11 indexed citations

Lee, Kyuho, Jinmook Lee, Sungpill Choi, & Hoi‐Jun Yoo. (2020). The Development of Silicon for AI: Different Design Approaches. IEEE Transactions on Circuits and Systems I Regular Papers. 67(12). 4719–4732. 13 indexed citations

Choi, Sungpill, Kyeongryeol Bong, Donghyeon Han, & Hoi‐Jun Yoo. (2019). CNNP-v2: A Memory-Centric Architecture for Low-Power CNN Processor on Domain-Specific Mobile Devices. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9(4). 598–611. 3 indexed citations

Im, Dongseok, Donghyeon Han, Sungpill Choi, Sanghoon Kang, & Hoi‐Jun Yoo. (2019). DT-CNN: Dilated and Transposed Convolution Neural Network Accelerator for Real-Time Image Segmentation on Mobile Devices. 29 indexed citations

Kim, Changhyeon, et al.. (2019). A 2.1TFLOPS/W Mobile Deep RL Accelerator with Transposable PE Array and Experience Compression. 136–138. 46 indexed citations

Choi, Sungpill, Jinsu Lee, Kyuho Lee, & Hoi‐Jun Yoo. (2018). A 9.02mW CNN-stereo-based real-time 3D hand-gesture recognition processor for smart mobile devices. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 220–222. 45 indexed citations

Lee, Juhyoung, Changhyeon Kim, Sungpill Choi, et al.. (2018). A 46.1 fps Global Matching Optical Flow Estimation Processor for Action Recognition in Mobile Devices. pp. 1–5. 1 indexed citations

Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, et al.. (2017). 14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on haar-like face detector. 248–249. 106 indexed citations

Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, & Hoi‐Jun Yoo. (2017). Low-Power Convolutional Neural Network Processor for a Face-Recognition System. IEEE Micro. 37(6). 30–38. 24 indexed citations

10.

Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, et al.. (2017). A 0.62mW Ultra-low-power Convolutional Neural Network Face Recognition Processor and a CIS Integrated with Always-on Haar-like Face Detector. 8 indexed citations

11.

Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, Donghyeon Han, & Hoi‐Jun Yoo. (2017). A Low-Power Convolutional Neural Network Face Recognition Processor and a CIS Integrated With Always-on Face Detector. IEEE Journal of Solid-State Circuits. 53(1). 115–123. 84 indexed citations

12.

Park, Seong‐Wook, Jun-Young Park, Kyeongryeol Bong, et al.. (2016). An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications. IEEE Transactions on Biomedical Circuits and Systems. 9(6). 1–1. 34 indexed citations

13.

Park, Seong‐Wook, et al.. (2016). 14.1 A 126.1mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses. 254–255. 22 indexed citations

14.

Yoo, Hoi‐Jun, Seong‐Wook Park, Kyeongryeol Bong, et al.. (2015). A 1.93 TOPS/W Scalable Deep Learning/Inference Processor with Tetra-parallel MIMD Architecture for Big Data Applications. 80–81. 34 indexed citations

15.

Park, Seong‐Wook, Kyeongryeol Bong, Dongjoo Shin, et al.. (2015). 4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications. 1–3. 78 indexed citations

16.

Choi, Sungpill, et al.. (2015). Development of Reefer Container Real-time Management System. The Journal of the Korean Institute of Information and Communication Engineering. 19(12). 2917–2923. 1 indexed citations

17.

Kim, Gyeonghoon, Sungpill Choi, & Hoi‐Jun Yoo. (2015). K-glass: Real-time markerless augmented reality smart glasses platform. 1712–1717. 1 indexed citations

18.

Kim, Gyeonghoon, Youchang Kim, Kyuho Lee, et al.. (2014). 10.4 A 1.22TOPS and 1.52mW/MHz augmented reality multi-core processor with neural network NoC for HMD applications. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 182–183. 10 indexed citations

19.

Lee, Eun‐Kyu, et al.. (2013). Design and Performance Evaluation for GPS/GLONASS Antenna used for Container tracking. The Journal of the Korean Institute of Information and Communication Engineering. 17(7). 1544–1550. 2 indexed citations

20.

Choi, Sungpill, et al.. (2009). IP-RFID based small ship management system. International Conference on Communications. 235–239. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact